REGE.FC {blockmodeling} | R Documentation |

REGE - Algorithms for compiting (dis)similarities in terms of regular equivalnece (White & Reitz, 1983).
`REGE, REGE.for`

- Classical REGE or REGGE, as also implemented in Ucinet. Similarities in terms of regular equivalence are computed. The `REGE.for`

is a wrapper for calling the FORTRAN subrutine written by White (1985a), modified to be called by R. The `REGE`

does the same, however it is written in R. The functions with and without ".for" differ only in whether they are implemented in R of FORTRAN. Needless to say, the functions implemented in FORTRAN are much faster.
`REGE.ow, REGE.ow.for`

- The above function, modified so that a best match is searched for each arc separately (and not for both arcs, if they exist, together).
`REGE.nm.for`

- REGE or REGGE, modified to use row and column normalized matrices instead of the original matrix.
`REGE.ownm.for`

- The above function, modified so that a best match for an outgoing ties is searched on row-normalized network and for incoming ties on column-normalized network.
`REGD.for`

- REGD or REGDI, a dissimilarity version of the classical REGE or REGGE. Dissimilarities in terms of regular equivalence are computed. The `REGD.for`

is a wrapper for calling the FORTRAN subroutine written by White (1985b), modified to be called by R.
`REGE.FC`

- Actually an earlier version of REGE. The difference is in the denominator. See Žiberna (2007) for details.
`REGE.FC.ow`

- The above function, modified so that a best match is searched for each arc separately (and not for both arcs, if they exist, together).
other - still in testing stage.

```
REGE.FC(
M,
E = 1,
iter = 3,
until.change = TRUE,
use.diag = TRUE,
normE = FALSE
)
REGE.FC.ow(
M,
E = 1,
iter = 3,
until.change = TRUE,
use.diag = TRUE,
normE = FALSE
)
REGE(M, E = 1, iter = 3, until.change = TRUE, use.diag = TRUE)
REGE.ow(M, E = 1, iter = 3, until.change = TRUE, use.diag = TRUE)
REGE.for(M, iter = 3, E = 1)
REGD.for(M, iter = 3, E = 0)
REGE.ow.for(M, iter = 3, E = 1)
REGD.ow.for(M, iter = 3, E = 0)
REGE.ownm.for(M, iter = 3, E = 1)
REGE.ownm.diag.for(M, iter = 3, E = 1)
REGE.nm.for(M, iter = 3, E = 1)
REGE.nm.diag.for(M, iter = 3, E = 1)
REGE.ne.for(M, iter = 3, E = 1)
REGE.ow.ne.for(M, iter = 3, E = 1)
REGE.ownm.ne.for(M, iter = 3, E = 1)
REGE.nm.ne.for(M, iter = 3, E = 1)
REGD.ne.for(M, iter = 3, E = 0)
REGD.ow.ne.for(M, iter = 3, E = 0)
```

`M` |
Matrix or a 3 dimensional array representing the network. The third dimension allows for several relations to be analyzed. |

`E` |
Initial (dis)similarity in terms of regular equivalnece. |

`iter` |
The desired number of iterations. |

`until.change` |
Should the iterations be stopped when no change occurs. |

`use.diag` |
Should the diagonal be used. If |

`normE` |
Should the equivalence matrix be normalized after each iteration. |

`E` |
A matrix of (dis)similarities in terms of regular equivalnece. |

`Eall` |
An array of (dis)similarity matrices in terms of regular equivalence, each third dimension represets one iteration. For ".for" functions, only the initial and the final (dis)similarities are returned. |

`M` |
Matrix or a 3 dimensional array representing the network used in the call. |

`iter` |
The desired number of iterations. |

`use.diag` |
Should the diagonal be used - for functions implemented in R only. |

...

Žiberna, A. (2008). Direct and indirect approaches to blockmodeling of valued networks in terms of regular equivalence. Journal of Mathematical Sociology, 32(1), 57-84. doi: 10.1080/00222500701790207

White, D. R., & Reitz, K. P. (1983). Graph and semigroup homomorphisms on networks of relations. Social Networks, 5(2), 193-234.

White, D. R.(1985a). DOUG WHITE'S REGULAR EQUIVALENCE PROGRAM. Retrieved from http://eclectic.ss.uci.edu/~drwhite/REGGE/REGGE.FOR

White, D. R. (1985b). DOUG WHITE'S REGULAR DISTANCES PROGRAM. Retrieved from http://eclectic.ss.uci.edu/~drwhite/REGGE/REGDI.FOR

White, D. R. (2005). REGGE. Retrieved from http://eclectic.ss.uci.edu/~drwhite/REGGE/

#' @author Aleš Žiberna based on Douglas R. White's original REGE and REGD

`sedist`

, `critFunC`

, `optParC`

, `plot.mat`

```
n <- 20
net <- matrix(NA, ncol = n, nrow = n)
clu <- rep(1:2, times = c(5, 15))
tclu <- table(clu)
net[clu == 1, clu == 1] <- 0
net[clu == 1, clu == 2] <- rnorm(n = tclu[1] * tclu[2], mean = 4, sd = 1) * sample(c(0, 1),
size = tclu[1] * tclu[2], replace = TRUE, prob = c(3/5, 2/5))
net[clu == 2, clu == 1] <- 0
net[clu == 2, clu == 2] <- 0
D <- REGE.for(M = net)$E # Any other REGE function can be used
plot.mat(net, clu = cutree(hclust(d = as.dist(1 - D), method = "ward.D"),
k = 2))
# REGE returns similarities, which have to be converted to
# disimilarities
res <- optRandomParC(M = net, k = 2, rep = 10, approaches = "hom", homFun = "ss", blocks = "reg")
plot(res) # Hopefully we get the original partition
```

[Package *blockmodeling* version 1.1.5 Index]